The emergence of video applications and video capable devices have contributed substantially to the increase of video traffic on Internet. New mechanisms recommending video rate adaptation towards delivering enhanced Quality of Experience (QoE) at the same time making room for more sessions. This paper introduces a cross-layer QoE-aware architecture for video traffic over the Internet. It proposes that video sources at the application layer adapt their rate to the network environment by controlling their transmitted bit rate dynamically; and the edge of network at the network layer protects the quality of the active video sessions by controlling the acceptance of new session through a video-aware admission control. In particular, it will seek the most efficient way of accepting new video session and adapting transmission rates to free up resources for more session while maintaining the QoE of active sessions. The proposed framework will contribute to the preparation for the extreme growth of video traffic in the foreseeable future. Simulation results show that the proposed cross-layer architecture guarantees the QoE for the admitted sessions and utilizes the link more efficiently comparing to the rate adaptation only architecture.

The test data can be found in Reference [22] in this manuscript.

Conventional techniques for noninvasive measurement of brain function such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) have critical limitations of spatial or temporal resolution. Here, we developed a novel technique which enables the precise measurement of dynamic brain signals and localized identification of active brain regions. In this technique, termed as magnetically biased field (MBF), human brain signal is measured as the fluctuation of a transcranial static magnetic field emitted by a coil placed on the scalp. The validity of MBF was confirmed by the measurement of somatosensory evoked signals. Fast somatosensory evoked signals were successfully observed. Localized maximum positive and negative deflections appeared at the region which represents the right primary somatosensory area contralateral to the stimulated hand. The ability of MBF to detect dynamic brain activity precisely can have numerous applications such as diagnosing brain diseases and brain-machine interfaces.

The rapid shift toward video on-demand and real time information systems has affected mobile as well as wired networks. The research community has placed a strong focus on optimizing the Quality of Experience (QoE) of video traffic, mainly because video is popular among Internet users. Techniques have been proposed in different directions towards improvement of the perception of video users. This paper investigates the performance of a novel cross-layer architecture for optimizing the QoE of video traffic. The proposed architecture is compared to two other architectures; non-adaptive and adaptive. For the former, video traffic is sent without adaptation, whereas for the later video sources adapt their transmission rate. Both are compared in terms of the mean opinion score of video sessions, number of sessions, delay, packet drop ratio, jitter and utilization. The results from extensive simulations show that the proposed architecture outperforms the non-adaptive and adaptive architectures for video traffic.

We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature spaces. STC learns subgraphs which have a star topology rather than learning a fixed graph like most spectral methods. Due to the properties of a star topology, STC is graph-scale free (without a fixed graph size constraint). It has fewer parameters in its convolutional filter and is inductive, so it is more flexible and can be applied to large and evolving graphs. The convolutional filter is learnable and localized, similar to CNNs in Euclidean feature spaces, and can share weights across graphs. To test the method, STC was compared with state-of-the-art graph convolutional methods in a supervised learning setting on nine node properties prediction benchmark datasets: Cora, Citeseer, Pubmed, PPI, Arxiv, MAG, ACM, DBLP, and IMDB. The experimental results showed that STC achieved state-of-the-art performance on all these datasets and maintained good robustness. In an essential protein identification task, STC outperformed state-of-the-art essential protein identification methods. An application of using pretrained STC as the embedding for feature extraction of some downstream classification tasks was introduced. The experimental results showed that STC can share weights across different graphs and be used as the embedding to improve the performance of downstream tasks.

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture ).

Transmission of video traffic over the Internet has grown exponentially in the past few years with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise the Quality of Experience (QoE) of end users’ video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted.

Since the initial reports of the Coronavirus surfacing in Wuhan, China; the novel virus currently without a cure has spread like a wildfire across the globe. The virus spread exponentially across all inhabited continent; catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyse and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modelling tools to latest AI technology; the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response – whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance including lockdowns. On one hand, this paper shows what accuracy of research brings to enable fighting this disease; while on the other hand it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt in compiling different research methods and comparing their accuracy in predicting the spread of COVID-19.

Low-power wide area (LPWA) technologies are strongly recommended as the underlying networks for Internet of Things (IoT) applications. They offer attractive features, including wide-range coverage, long battery life, and low data rates. This paper reviews the current trends in this technology, with an emphasis on the services it provides and the challenges it faces. The industrial paradigms for LPWA implementation are presented. Compared with other work in the field, this paper focuses on the need for integration among different LPWA technologies and recommends the appropriate LPWA solutions for a wide range of IoT application and service use cases. Opportunities created by these technologies in the market are also analyzed. The latest research efforts to investigate and improve the operation of LPWA networks are also compared and classified to enable researchers to quickly get up to speed on the current status of this technology. Finally, challenges facing LPWA are identified and directions for future research are recommended.

The internet of smart devices is a network of intelligent gadgets with sensors, programs, Wi-Fi and communication network connections. These devices store the data in cloud and process data outside the device using the proposed Cloud-Internet communication framework. These devices can communicate with other devices using the proposed framework. However, there are many challenges for communication security among the internet of smart devices. The Cloud can store the device data with security, reliability, privacy and service availability. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud computing. Our purpose of this study is to create a framework for providing the communication security among smart devices network for the internet of things using cloud computing. Our main contribution links a new study for providing communication security for the internet of smart devices using the cloud-Internet framework. This study can be helpful for communication security problem in the framework of the Internet of Things. The proposed study generates a new framework for solving the issue of communication security among internet of smart devices.

With the inevitable dominance of video traffic on the Internet, providing perceptually good video quality is becoming a challenging task. This is partly due to the bursty nature of video traffic, changing network conditions and limitations of network transport protocols. This growth of video traffic has made Quality of Experience (QoE) of the end user the focus of the research community. In contrast, Internet service providers are concerned about maximizing revenue by accepting as many sessions as possible, as long as customers remain satisfied. However, there is still no entirely satisfactory admission algorithm for flows with variable rate. The trade-off between the number of sessions and perceived QoE can be optimized by exploiting the bursty nature of video traffic. This paper proposes a novel algorithm to determine the upper limit of the aggregate video rate that can exceed the available bandwidth without degrading the QoE of accepted video sessions. A parameter $\beta$ that defines the exceedable limit is defined. The proposed algorithm results in accepting more sessions without compromising the QoE of on-going video sessions. Thus it contributes to the optimization of the QoE-Session trade-off in support of the expected growth of video traffic on the Internet.

In the next generation of computing, Mobile ad-hoc network (MANET) will play a very important role in the Internet of Things (IoT). The MANET is a kind of wireless networks that are self-organizing and auto connected in a decentralized system. Every device in MANET can be moved freely from one location to another in any direction. They can create a network with their neighbors’ smart devices and forward data to another device. The IoT-Cloud-MANET framework of smart devices is composed of IoT, cloud computing, and MANET. This framework can access and deliver cloud services to the MANET users through their smart devices in the IoT framework where all computations, data handling, and resource management are performed. The smart devices can move from one location to another within the range of the MANET network. Various MANETs can connect to the same cloud, they can use cloud service in a real time. For connecting the smart device of MANET to cloud needs integration with mobile apps. My main contribution in this research links a new methodology for providing secure communication on the internet of smart devices using MANET Concept in 5G. The research methodology uses the correct and efficient simulation of the desired study and can be implemented in a framework of the Internet of Things in 5G.

Blockchain (BC) in the Internet of Things (IoT) is a novel technology that acts with decentralized, distributed, public and real-time ledger to store transactions among IoT nodes. A blockchain is a series of blocks, each block is linked to its previous blocks. Every block has the cryptographic hash code, previous block hash, and its data. The transactions in BC are the basic units that are used to transfer data between IoT nodes. The IoT nodes are different kind of physical but smart devices with embedded sensors, actuators, programs and able to communicate with other IoT nodes. The role of BC in IoT is to provide a procedure to process secured records of data through IoT nodes. BC is a secured technology that can be used publicly and openly. IoT requires this kind of technology to allow secure communication among IoT nodes in heterogeneous environment. The transactions in BC could be traced and explored through anyone who are authenticated to communicate within the IoT. The BC in IoT may help to improve the communication security. In this paper, I explored this approach, its opportunities and challenges.

In this paper, a Hewlett-Packard (HP) memristor model with a new window function and its versatile characteristics are presented. SPICE behaviors of the linear and nonlinear memristor model are studied through PSpice simulation. High flexibility is demonstrated for emulating the behaviors of the practical HP memristors. Furthermore, the characteristics of the composite SPICE behaviors are both investigated when two memristors are connected in series and in parallel. The polarity of each memristor is also taken into consideration. The relationships among flux, charge, voltage, current, and memristance of the double memristor circuits are simulated and analyzed.

This paper reports the findings of a study of an optoelectronic oscillator (OEO) with large delay under proportional and proportional integral control by a phase-locked loop (PLL). The study is the first to fully account for the OEO delay in responding to a tuning stimulus including the location of all the countable infinity of poles of the system function.

This study analyzed highly-correlated, feature-rich datasets from hyperspectral remote sensing data using multiple machine and statistical-learning methods. The effect of filter-based feature-selection methods on predictive performance was compared. Also, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%) was modeled as a function of reflectance, and variable importance was assessed using permutation-based feature importance. Overall support vector machine (SVM) outperformed others such as random forest (RF), extreme gradient boosting (XGBoost), lasso (L1) and ridge (L2) regression by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than the unfiltered feature sets, while ensemble filters did not have a substantial impact on performance. Permutation-based feature importance estimated features around the red edge to be most important for the models. However, the presence of features in the near-infrared region (800 nm - 1000 nm) was essential to achieve the best performances. More training data and replication in similar benchmarking studies is needed for more generalizable conclusions. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies.

COVID-19 has emerged as a highly contagious disease which has caused a devastating impact across the world with a very large number of infections and deaths. Timely and accurate testing is paramount to an effective response to this pandemic as it helps identify infections and therefore mitigate (isolate/cure) them. In this paper, we investigate this challenge and contribute by presenting a blockchain-based solution that incorporates self-sovereign identity, re-encryption proxies, and decentralized storage, such as the interplanetary file systems (IPFS). Our solution implements digital medical passports (DMP) and immunity certificates for COVID-19 test-takers. We present smart contracts based on the Ethereum blockchain written and tested successfully to maintain a digital medical identity for test-takers that help in a prompt trusted response directly by the relevant medical authorities. We reduce the response time of the medical facilities, alleviate the spread of false information by using immutable trusted blockchain, and curb the spread of the disease through DMP. We present a detailed description of the system design, development, and evaluation (cost and security analysis) for the proposed solution. Since our code leverages the use of the on-chain events, the cost of our design is almost negligible. We have made our smart contract codes publicly available on Github.

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption. In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. In this paper, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user c omfort simultaneously. Accordingly, we develop the control strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control strategy for HVAC systems that balances between electricity cost and user comfort.